Space-ground Fluid AI for 6G Edge Intelligence
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Abstract (arXiv)
Edge artificial intelligence (AI) and space-ground integrated networks (SGINs) are two main usage scenarios of the sixth-generation (6G) mobile networks. Edge AI supports pervasive low-latency AI services to users, whereas SGINs provide digital services to spatial, aerial, maritime, and ground users. This article advocates the integration of the two technologies by extending edge AI to space, thereby delivering AI services to every corner of the planet. Beyond a simple combination, our novel framework, called space-ground fluid AI, leverages the predictive mobility of satellites to facilitate fluid horizontal and vertical task/model migration in the networks. This ensures non-disruptive AI service provisioning in spite of the high mobility of satellite servers. The aim of the article is to introduce the (space-ground) fluid AI technology. First, we outline the network architecture and unique characteristics of fluid AI. Then, we delve into three key components of fluid AI, i.e., fluid learning, fluid inference, and fluid model downloading. They share the common feature of coping with satellite mobility via inter-satellite and space-ground cooperation to support AI services. Finally, we discuss the considerations for the real-world deployment of fluid AI and identify further research opportunities.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Nov 2024.
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Citing papers per year: 2024: 1; 2025: 4; 2026: 3.
8 papers.
- 2025Revisiting Outage for Edge Inference Systemscited by 5
- 2025Space-O-RAN: Enabling Intelligent, Open, and Interoperable Non Terrestrial Networks in 6Gcited by 4
- 2025PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inferencecited by 4
- 2026SpaceMoE: Realizing Distributed Mixture-of-Experts Inference over Space Networkscited by 3
- 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networkscited by 2
- 2025Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneitycited by 2
- 2026Space Generative AI with Solar Energy Harvestingcited by 0
- 2026From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivitycited by 0
- 2026From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity9 Sep 2026
- 2026Space Generative AI with Solar Energy Harvesting1 Sep 2026
- 2026SpaceMoE: Realizing Distributed Mixture-of-Experts Inference over Space Networks1 May 2026
- 2025Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity21 Jul 2025
- 2025PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inference29 Mar 2025
- 2025Revisiting Outage for Edge Inference Systems22 Mar 2025
- 2025Space-O-RAN: Enabling Intelligent, Open, and Interoperable Non Terrestrial Networks in 6G21 Feb 2025
- 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks23 Dec 2024